Management Article

Taran March @ Quality Digest’s picture

By: Taran March @ Quality Digest

So it seems the contentious wall along our southern border, variously known as the Trump wall or the Mexico-United States barrier, isn’t meeting requirements. Walls keep people in; walls keep people out. They serve as backdrops for graffiti. But aside from fulfilling the last item, this wall might more accurately be called a solution for the wrong problem. Time, and past time, for quality assurance folks to step in.

Richard Ruiz’s picture

By: Richard Ruiz

According to the Deloitte Automotive Quality 2020 report, auto manufacturers spend an average of 116 days annually on quality management system (QMS) compliance.

Layered process audits (LPAs), which can number more than a thousand audits per year, can take up many of those hours for companies that perform these short, frequent checks.

Executed correctly, LPAs can help sharply reduce defects and quality costs relatively quickly, but these high-frequency audits can also bury companies in administrative work if they’re not prepared.

This article examines classic problems standing in the way of quality, and how to fix them to make bigger and faster improvements.

Scheduling inefficiencies

Making and sticking to a schedule is fundamental to LPAs success, but the reality of scheduling daily, shift-level audits of critical to quality processes can quickly become overwhelming. Scheduling around paid time off and planned downtime is more complex and time-consuming, as is notifying auditors of their responsibilities.

Ryan E. Day’s picture

By: Ryan E. Day

Headquartered in Houston, Texas, Dimensional Engineering was born on the back of a dream, a major contract from an aircraft manufacturer, and a process developed specifically to fulfill that project. Dimensional Engineering has steadily grown to become a full-service team of consulting and field metrologists, focused on the application of 3D metrology services. With aerospace and automotive applications firmly established, Dimensional Engineering has expanded into the fields of gas and oil, while positioning itself to tackle marine applications as well.


Equipment at gas and oil facilities present a unique challenge in that many system components involve precision-machined interior features, but a rough casting on the outer surfaces. In addition, the cast pieces present numerous compound curves and varying wall thicknesses. This means that many components in an oil and gas system are an inspection nightmare, and traditional tools are often incapable of providing the quality dimensional data necessary for repairs and reverse engineering.

Janelle Farkas’s picture

By: Janelle Farkas

According to the International Institute for Analytics, businesses that use data will gain $430 billion in productivity benefits over competitors who aren’t using data by 2020. As an industrial engineer for the Northeastern Pennsylvania Industrial Resource Center, part of the MEP National Network, I tell small-business owners and manufacturers that this quote does not say you have to use “big” data. You don’t have to use complex analysis methods and the latest and greatest technology. It just says in order to get a piece of that productivity pie, you have to do something.

Unfortunately, many small- to medium-sized manufacturers (SMMs) are still not taking advantage of data to boost their bottom-line margins. This is often due to a common misconception that utilizing data requires a Ph.D. in statistics or a state-of-the-art ERP system to crunch the information for you. Utilizing data for manufacturing does require a willingness to experiment and a time investment to realize bottom-line benefits, but it doesn’t have to be complicated.

Theodoros Evgeniou’s picture

By: Theodoros Evgeniou

It seems that every week, AI technology has learned to do something humans do, but faster and better. From detecting cancers and eye conditions to predicting floods; or analyzing the language, tone, and facial expressions of candidates during recruitment processes, AI is now at the stage where it not only supports human judgment, but also makes increasingly more complex and accurate decisions.

As technology further improves and we learn how to better work and collaborate with AI, interactions between humans and computers will significantly enhance creativity—of both humans and bots.

Martin Abel’s picture

By: Martin Abel

Imagine that your boss Ethan calls you into his office. He expresses disappointment in your recent performance and lack of commitment. How would you react? Accept the feedback and put in more effort? Would you pout in your office and start looking for a new job?

Now, would your reaction be different if your boss was not named Ethan but Emily?

I’m a professor of economics, and my research investigates this very question. We hired 2,700 workers online to transcribe receipts, randomly assigning a male or female name to a manager, and randomly assigning which workers would receive performance feedback.

Results show that both women and men react more negatively to criticism if it comes from a woman. Our subjects reported that criticism by a woman led to a larger reduction in job satisfaction than criticism by a man. Employees were also doubly disinterested in working for the firm in the future if they had been criticized by a female boss.

David Moser’s picture

By: David Moser

Technology companies are frequently driven by their engineering processes. Of course product quality is regarded as most important, and that quality can be tested and measured with numbers and data. Such companies also frequently align their core identity with the engineering that belies their innovation. Their top executives often started out as engineers and keep looking primarily through their engineering lens as they become company leaders. Although it makes perfect sense, this approach is misguided.

Jody Muelaner’s picture

By: Jody Muelaner

One of the key ideas in lean manufacturing is that defects should be detected as early as possible. Efforts to control manufacturing processes, so that issues can be detected before defects occur, actually predate lean. Statistical process control (SPC) is a set of methods first created by Walter A. Shewhart at Bell Laboratories during the early 1920s. W. Edwards Deming standardized SPC for U.S. industry during WWII and introduced it to Japan during the American occupation after the war. SPC became a key part of Six Sigma, the Toyota Production System (TPS), and by extension, lean manufacturing.

SPC measures the outputs of processes, looking for small but statistically significant changes, so that corrections can be made before defects occur. SPC was first used within manufacturing, where it can greatly reduce waste due to rework and scrap. It can be used for any process that has a measurable output, and SPC is now widely used in service industries and healthcare.

Tom Comstock’s picture

By: Tom Comstock

How can industrial and manufacturing enterprises achieve better new product introduction (NPI), a critical element of operational excellence? Corporate goals of improving market share and revenue, maintaining competitive differentiators, and improving customer experiences are especially challenging when developing and launching new products—making it vitally important that NPI is seamless and high quality. Despite significant investment in NPI, a startling 44 percent of new products fail to meet most NPI success criteria.

Manufacturers and industrials face three key challenges:
• Organizational and data siloes, with little collaboration among increasingly complex supplier networks
• Core process deficiencies, as shown by key performance metrics, even as solutions are within reach
• Outdated or poorly integrated operations and quality systems, and data sources

Joseph Paris’s picture

By: Joseph Paris

A few years ago, I was asked to conduct a workshop, deliver a keynote, and chair a three-day conference on manufacturing process excellence in Europe, produced by the Process Excellence Network (PEX), a division of IQPC. Although that was a lot to ask of me, the lineup of speakers and content was pretty strong, and I was looking forward to gaining knowledge as much as I was to delivering what I had to offer.

During the conference, I had the opportunity to meet one of the speakers, who was the director of operational excellence in Europe for a publicly-traded company—which is not so unusual, as most of the speakers and attendees were in operational excellence (or continuous improvement) leadership roles. He was a bright and passionate individual for sure, and we promised to have a follow-on conversation in a month’s time.

When it came time for the follow-up call, the much learned and passionate individual told me that he had been released. Being rather shocked, I asked what had happened. He told me the company had killed the entire operational excellence program to “cut costs.” Hm... and so it goes.

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